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Finding Nemo: Predicting Movie Performances by Machine Learning Methods

Author

Listed:
  • Jong-Min Kim

    (Statistics Discipline, Division of Science and Mathematics, University of Minnesota-Morris, Morris, MN 56267, USA)

  • Leixin Xia

    (Department of Biostatistics and Data Science, University of Texas Health Science Center, Houston, TX 77030, USA)

  • Iksuk Kim

    (Department of Marketing, California State University, Los Angeles 5151 State University Dr, Los Angeles, CA 90032, USA)

  • Seungjoo Lee

    (Department of Big Data and Statistics, Cheongju University, Chungbuk 28503, Korea)

  • Keon-Hyung Lee

    (Askew School of Public Administration and Policy, Florida State University, Tallahassee, FL 32306-2250, USA)

Abstract

Analyzing the success of movies has always been a popular research topic in the film industry. Artificial intelligence and machine learning methods in the movie industry have been applied to modeling the financial success of the movie industry. The new contribution of this research combined Bayesian variable selection and machine learning methods for forecasting the return on investment (ROI). We also attempt to compare machine learning methods including the quantile regression model with movie performance data in terms of in-sample and out of sample forecasting.

Suggested Citation

  • Jong-Min Kim & Leixin Xia & Iksuk Kim & Seungjoo Lee & Keon-Hyung Lee, 2020. "Finding Nemo: Predicting Movie Performances by Machine Learning Methods," JRFM, MDPI, vol. 13(5), pages 1-12, May.
    • Handle: RePEc:gam:jjrfmx:v:13:y:2020:i:5:p:93-:d:355781
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    References listed on IDEAS

    as
    1. Kim, Taegu & Hong, Jungsik & Kang, Pilsung, 2015. "Box office forecasting using machine learning algorithms based on SNS data," International Journal of Forecasting, Elsevier, vol. 31(2), pages 364-390.
    2. Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 2018. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 20(3), pages 577-588, June.
    3. Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 0. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-12.
    4. Yan Liu & Tian Xie, 2019. "Machine learning versus econometrics: prediction of box office," Applied Economics Letters, Taylor & Francis Journals, vol. 26(2), pages 124-130, January.
    5. Eliashberg, Jehoshua & Hegie, Quintus & Ho, Jason & Huisman, Dennis & Miller, Steven J. & Swami, Sanjeev & Weinberg, Charles B. & Wierenga, Berend, 2009. "Demand-driven scheduling of movies in a multiplex," International Journal of Research in Marketing, Elsevier, vol. 26(2), pages 75-88.
    6. Legoux, Renaud & Larocque, Denis & Laporte, Sandra & Belmati, Soraya & Boquet, Thomas, 2016. "The effect of critical reviews on exhibitors' decisions: Do reviews affect the survival of a movie on screen?," International Journal of Research in Marketing, Elsevier, vol. 33(2), pages 357-374.
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    Cited by:

    1. Joshua Eklund & Jong-Min Kim, 2022. "Examining Factors That Affect Movie Gross Using Gaussian Copula Marginal Regression," Forecasting, MDPI, vol. 4(3), pages 1-14, July.

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